Description Arguments Author(s) See Also Examples

Results from `distinct_test`

function

`res` |
contains a |

Simone Tiberi simone.tiberi@uzh.ch

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
# load the input data:
# data("Kang_subset", package = "distinct")
# Kang_subset
#
# create the design of the study:
# samples = Kang_subset@metadata$experiment_info$sample_id
# group = Kang_subset@metadata$experiment_info$stim
# design = model.matrix(~group)
# rownames of the design must indicate sample ids:
# rownames(design) = samples
# design
#
# Note that the sample names in `colData(x)$name_sample` have to be the same ones as those in `rownames(design)`.
# rownames(design)
# unique(SingleCellExperiment::colData(Kang_subset)$sample_id)
#
# In order to obtain a finer ranking for the most significant genes, if computational resources are available, we encourage users to increase P_4 (i.e., the number of permutations when a raw p-value is < 0.001) and set P_4 = 20,000 (by default P_4 = 10,000).
#
# The group we would like to test for is in the second column of the design, therefore we will specify: column_to_test = 2
#
# set.seed(61217)
# res = distinct_test(
# x = Kang_subset,
# name_assays_expression = "logcounts",
# name_cluster = "cell",
# design = design,
# column_to_test = 2,
# min_non_zero_cells = 20,
# n_cores = 2)
#
# save(res, file = "res.RData")
# saveRDS(res, file = "res.rds")
``` |

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